1. Field of Invention
The present invention relates to a method of generating a feature, and more generally to a method of generating an assistant feature.
2. Description of Related Art
In the very advanced fabrication technology of integrated circuits, to reduce the dimensions of devices and to increase the degree of integration are leading trends and topics for further development. The photolithography process is one of the most crucial factors to determine the reliability and performance of devices. The increased degree of integration causes the shrinkage of devices and a reduced distance between devices. A deviation is thus easily caused while transferring a feature from a photomask to a layer on a wafer. For example, when a mask feature is transferred to a layer on a wafer using a photolithography process, the angles on the feature become less sharp, the tail of the feature shrinks, and line-width increases or decreases etc. This is what is known as the Optical Proximity Effect (OPE). In the integrated circuits with a greater device dimension or a lower degree of integration, this deviation does not have an extremely adverse effect. However, in the integrated circuits with a higher degree of integration, this deviation may deteriorate the performance of devices. For example, in high-integration ICs, the distance between devices is small. Thus, when the line widths of features transferred to the wafer expand unexpectedly, the features may partially overlap to cause a short circuit.
Therefore, in the integrated circuits with a higher degree of integration, the Optical Proximity Correct (OPC) is adopted to resolve the Optical Proximity Effect (OPE). The Optical Proximity Correct (OPC) uses, for example, rule-based or model-based approaches to generate assistant features on the photomask. In a photolithography process, the assistant features can produce destructive interference to the light, and thus accurate features are formed on a substrate after exposure. However, most of the assistant features generated by the existing model-based approach are extremely accurate assistant features calculated by the program. Therefore, it takes a long time to calculate the accurate assistant features in the model-based approach.